| from transformers import TrainingArguments | |
| training_args = TrainingArguments( | |
| output_dir="detr-resnet-50_finetuned_cppe5-second", | |
| per_device_train_batch_size=8, | |
| num_train_epochs=30, # Mise à jour pour correspondre à num_epochs: 100 | |
| fp16=False, | |
| save_steps=200, | |
| logging_steps=50, | |
| learning_rate=1e-5, | |
| weight_decay=1e-4, | |
| save_total_limit=2, | |
| remove_unused_columns=False, | |
| push_to_hub=True, | |
| seed=42, # Ajout de seed: 42 | |
| lr_scheduler_type="linear", # Mise à jour pour correspondre à lr_scheduler_type: linear | |
| optim="adamw_torch", # Optimizer Adam avec betas et epsilon définis ci-dessous | |
| ) | |
| # Pour spécifier les paramètres de l'optimiseur Adam, vous pouvez les passer lors de la création de l'optimiseur dans la fonction d'entraînement | |
| from transformers import AdamW | |
| optimizer = AdamW(model.parameters(), lr=1e-5, betas=(0.9, 0.999), eps=1e-08) | |
| IoU metric: bbox | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.116 | |
| Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.193 | |
| Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.125 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.025 | |
| Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.115 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.102 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.196 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.239 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.052 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.115 | |
| Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.227 |